The relationship between them may depend on who accrues income from the new inventions and what their additional consumption is

Existing advances in technology from smartphones to new car services affect our everyday lives. Yet aggregate productivity has been growing very sluggishly. In 2016 and 2017, for example, output per hour in the US non-farm business sector rose by less than 1% per year on average.

The disconnect between productivity growth and the technology revolution has triggered a sharp debate in economics. A scintillating new paper by Adair Turner of the Institute for New Economic Thinking suggests that rather than presenting a puzzle, the combination of technological innovation and low measured productivity growth is exactly what we should expect.

Before turning to Turner’s argument, it’s worth revisiting previous attempts to resolve the apparent puzzle. One perspective argues that slow productivity growth is at least partly a mirage. For example, if new inventions improve the quality of goods and services but the improvements are not properly incorporated into the economic statistics, the result would be that measured productivity is lower than actual productivity. The challenge is to determine whether the measurement errors are any bigger today than in the past (if not, they could not explain the deceleration in measured productivity) and how large they plausibly are in any case. Some new research suggests that the errors may be growing meaningfully larger, but most studies suggest that any effect is too small to explain the bulk of the productivity slowdown.

A second argument is that there is a lag before new technologies raise productivity, because businesses need to adjust operations to take advantage of them and that takes time. According to this perspective, we are still in the interregnum.

A third perspective attributes the phenomenon to sand in the wheels of the economy, as reflected in the decline in geographic mobility and the rising gap between leading firms and others in the same sector. At frontier firms, productivity growth has not declined, which raises the question of why those advances are not spilling over to other companies. Jason Furman and I explore this decline in dynamism and the growing gaps between firms in a recent working paper for the Peterson Institute for International Economics.

None of these arguments have thus far resolved the productivity puzzle. Enter Turner, who has punctured many economic debates, from pensions to climate change. He writes that “it is quite possible that an acceleration in underlying technological progress, which allows us to achieve dramatic productivity improvement in existing production processes, can be accompanied by a decline in total measured productivity”.

In other words, there is really no puzzle to explain.

The core of Turner’s argument is that the impact of new technology on total productivity growth depends on who accrues the income from the new inventions; what additional consumption they choose to enjoy with that income; and the nature of productivity advances in the sectors that workers are shifted into as a result. In particular, if those who directly accrue income from the new inventions choose to consume more services (such as personal services or artistic ones) that are hard to automate, the net result could be the coexistence of rapid technological progress and slow or non-existent overall productivity growth.

So technological progress and productivity growth have tended to coexist in the past because the workers shifted from one sector (say, farming) to another (manufacturing) and in both the sender and recipient sector rapid productivity growth was occurring.

What would happen, though, if the recipient sectors suffer from “Baumol’s disease” which features limited potential for productivity improvements because it is hard to replace people with machines in those areas? Then, aggregate productivity growth will not march in lockstep with technological progress.

Furthermore, as our incomes rise, we may demand more services with Baumol’s disease characteristics. The employment projections from the Bureau of Labor Statistics highlight the point. The top four occupations ranked by the number of new jobs projected to be created between 2016 and 2026 are personal care aides, cooks and servers, registered nurses and home health aides. In all four cases, the service provided involves person-to-person interactions that are, at least for now, difficult to automate.

Turner’s analysis is much broader than just the impact of such sector-shifting, but that forms an important pillar of his argument. While there’s no doubt he may be right in theory, the question is how important this phenomenon is to the aggregate productivity puzzle. One piece of evidence comes from a recent McKinsey report, which estimates that productivity growth declined by 0.2 percentage points per year between 1987 and 2014 “as employment transitioned from high-productivity manufacturing sectors to lower-productivity sectors such as health care and administrative and support services”.

The McKinsey numbers suggest that, at least until recently, Turner’s argument does not fully eliminate the empirical productivity puzzle. But Turner is focused on the next several decades, and over that period he may prove to be increasingly correct—unless we see automation dramatically changing how services ranging from personal care to education to healthcare can be delivered.